Investigating cis-Elements of Microtubule-Associated Protein Tau's 3'-Untranslated Region

Daniel Montagna, The College of Wooster

Abstract

As of 2011, Alzheimer's disease (AD) is the sixth most common cause of death in the United States. Like many other neurodegenerative diseases, AD exhibits dysregulation of the protein microtubule-associated protein tau. Work by Roberson and colleagues in 2007 revealed that knocking out tau in a transgenic mouse model of AD drastically improved learning and memory compared to AD mice with wild-type tau, suggesting that a reduction in tau could be an effective treatment for AD. In an effort to discover cis-regulatory elements of tau as potential targets for manipulation of its expression in humans, we examined tau's relatively understudied 3' untranslated region (3'-UTR). Additionally, we investigated the role of TDP-43 and microRNA miR-34a as potential trans-acting factors that might modulate tau expression. By cloning five fragments of the tau 3'-UTR into a luciferase reporter construct, we previously determined by luciferase assay in human embryonic kidney (HEK) cells which fragments might contain expression-modulating cis-elements. This study attempted to replicate this data in a more relevant neuronal model: The SH-SY5Y neuroblastoma cell line. Attempts to knock down TDP-43 via RNAi failed to result in knockdown, and the transfection control for luciferase assays, Renilla luciferase, repeatedly failed to express above background. Transfection of a GFP-expression plasmid in both HEK and SH-SY5Y cells revealed that poor transfection efficiency in SH-SY5Y was responsible for these phenomena, with 50% and 8% of total cells expressing GFP in HEK and SH-SY5Y, respectively. Extrapolating from our data, we hypothesize that the integrity of miR-34a's binding region is essential for the expression-suppressing function of tau 3'-UTR fragment 954-1869. Following the results in SH-SY5Y, we investigated alternate neuronal cell culture models, and found that M17D cells may be a more suitable model for future work.

 

© Copyright 2013 Daniel Montagna